Forgive and Forget - Who Gets Credit After Bankruptcy and Why

8/11/2019 Forgive and Forget - Who Gets Credit After Bankruptcy and Why

1/37

Forgive and Forget: Who Gets Credit after Bankruptcy and Why?

Ethan Cohen-ColeUniversity of Maryland - College Park

Robert H Smith School of Business

Burcu Duygan-BumpFederal Reserve Bank of Boston

Judit Montoriol-Garriga

Federal Reserve Bank of Boston

July 23, 2009

Abstract

Conventional wisdom holds that individuals who have gone bankrupt face difculties getting creditfor at least some time. However, there is very little non-survey based empirical evidence on the availabil-

ity of credit post-bankruptcy and its dependence on the credit cycle. Using data from one of the largest

credit bureaus in the US, this paper makes three contributions. First, we show that individuals who le

for bankruptcy are indeed penalized with limited credit access post-bankruptcy, but we nd that this con-

sequence is very short lived. Ninety percent of individuals have access to some sort of credit within the

18 months after ling for bankruptcy, and 75% are given unsecured credit. Second, we show that those

individuals who have the easiest access to credit after bankruptcy tend to be the ones who have shown

previously the least ability and least propensity to repay their debt. In fact, a signicant fraction of indi-

viduals at the bottom of the credit quality spectrum seem to receive more credit after ling. We interpretthe widespread post-bankruptcy credit access and the differential credit provision across borrower types

as evidence that lenders target riskier borrowers. Employing a simple theoretical framework we show

that this interpretation is consistent with a prot maximizing lender whose optimal strategy involves seg-

menting borrowers by observable credit quality and bankruptcy status This interpretation is also in line


2/37

1 Introduction

The last two decades have seen a massive increase in both consumer credit and personal bankruptcies.

Policymakers and academics have attempted to understand the sources of these trends and the causal link

between them. As part of this debate, there has been much discussion about whether bankrupt individuals

are (or should be) excluded from credit markets, and whether these individuals have gone bankrupt due to

demand side factors, such as income and employment shocks, or as part of a general trend of increased credit

supply. Gross and Souleles (2002) argue that demand side factors play a more important role than those on

the supply side by showing that the changes in default rates are not caused by changes in the risk composi-

tion of borrowers. More recently, Dick and Lehnert (2009) have suggested that increased bankruptcies are a

consequence of increased competition in the banking sector. They argue that improved credit scoring algo-

rithms have helped banks compete and have increased lending to riskier households, which has led to a rise

in bankruptcies. In this paper, we seek to rene the supply-side story to better understand the consequences

of ling for bankruptcy by studying the availability of credit to households post-bankruptcy. Understanding

the consequences of ling provides insights into the incentives and determinants to le. This question is

important for understanding the implications of the credit card legislation recently signed into law, which

limits the penalizing strategies banks have previously used to generate signicant income, particularly from

riskier borrowers.1

Our results provide the most detailed picture to date of credit access for post-bankruptcy consumers.

We have three principal contributions to the literature. We nd broadly that credit availability does decline,


3/37

borrowers rather than to all borrower types.

While there have been many theoretical studies analyzing these questions, there is very little empirical

evidence, especially regarding facts about credit access post-bankruptcy. The economics literature, in par-

ticular, the macro-quantitative models of bankruptcy mostly assume an exclusion penalty where individuals

are not allowed to borrow post-bankruptcy for a given period of time. The legal literature on the other hand

suggests that there is relatively easy access to credit, relying principally on survey evidence. We discuss

both these lines of research in detail in the section below.

The aim of this paper is to contribute to this debate by investigating the degree to which individuals

that le for personal bankruptcy have access to credit markets afterwards. To our knowledge, this is the

rst study of post-bankruptcy credit access based on a nationally representative sample of consumer credit

information that is drawn from lenders themselves.2 Using panel data provided by a large US credit bureau

data, we establish some basic facts about the availability of credit post-bankruptcy and provide a related

discussion about the potential behavior of lenders consistent with our empirical results. We focus primarily

on access to unsecured lending as measured by credit limits on revolving credit lines, such as credit cards.

Using an empirical methodology to estimate the counterfactual credit for bankrupt borrowers if they

had not led for bankruptcy, we rst show that, on average, households are indeed `punished' for having

gone bankrupt through limited credit access. However we also show that this reduced credit availability is

very short-lived. Indeed, 90% of individuals have access to some sort of credit within the 18 months afterling for bankruptcy, and 75% have access to revolving credit. Second, and more interestingly, we nd

that access to credit after bankruptcy is highly heterogeneous: a signicant proportion of the population


4/37

debts prior to declaring bankruptcy and the least to access nancial or educational resources seem more

likely to experience an increase in their credit limits after ling for bankruptcy. Third, we also show that

these results are highly dependent on the aggregate credit environment: the large differences in credit access

between good and bad creditworthy bankrupts observed during booming credit years of 2003 and 2004

become much less signicant as the credit crunch begins in 2007. That is, the lowest credit score individuals

experience the largest change in credit access post-bankruptcy with the credit cycle.

We interpret these ndings that lenders differentiate credit supply both as a function of credit quality

and bankruptcy status. This interpretation is consistent with some of the survey evidence provided by

legal studies as discussed in Section 2 below that nd evidence that lenders quickly offer credit even to

low credit quality borrowers after bankruptcy. A recent NY Times article also provides a discussion on

how the credit card industry has relied on riskier households as a signicant source of revenue through

penalty interest rates and fees.3 Moreover, this interpretation is also supported by economic theory. To

show this and more importantly, to help us better interpret these ndings in a more structured fashion,

we build a simple theoretical framework to help understand lenders' decisions and debt valuation. We

then use this framework to illustrate that our empirical ndings are consistent with a prot maximizing

lender that differentiates lending decisions by borrowers, as segmented by credit score and bankruptcy

status. Two pieces of intuition emerge from our framework. First, lenders have no incentive to reduce

borrowers' credit limit unless bankruptcy reveals a change in a borrower's likelihood of repayment in thefuture or changes recovery rates post default. Second, from an economic perspective, declaring bankruptcy

can provide creditors with information about a borrower's ability or willingness to repay debt. Using our


5/37

lending decisions depend on the credit cycle. We argue that in a credit crunch the repayment ability of

the low quality borrowers is highly impaired, specially after bankruptcy, while for prime borrowers default

probabilities are just slightly increased. Additionally, since the credit cycle is closely related to the business

cycle, recovery rates on defaulted debt tend to decrease in a downturn. In consequence, lending to bankrupt

low quality borrowers in downturn periods is not as protable than in credit booms. Our empirical analysis

shows that by the end of 2007 bankrupt subprime borrowers faced more difculties to access the credit

market than in 2004, while access to credit for bankrupt prime borrowers is largely unchanged with the

credit cycle. This is consistent with the anecdotal evidence provided in a recent NY Times article that the

value of debt by non-payers is much higher in a boom than in a recession.5

The remainder of the paper is organized as follows. In Section 2 we provide a short summary of the

economics and legal literature on personal bankruptcy. Both these literature reviews are limited in scope, but

intended to provide a baseline for our discussion. In Section 3 we provide a simple stylized model of lender

decisions. Section 4 describes our dataset, while Section 5 presents the methodology we use to assess credit

availability post bankruptcy, and our results. We follow this with a short section discussing some potential

caveats to the analysis in section 6. Section 7 concludes the paper.

2 Literature on Personal Bankruptcy

Economics and Finance Literature

Following the dramatic rise in bankruptcies over the last couple of decades and the surrounding policy

discussions many researchers have attempted to study household bankruptcy decisions and tried to explain


6/37

then acquiring new debt. Of course, debt renegotiation does occur and nothing prevents a credit issuer from

providing credit to a bankrupt ex-post. The presence of an exclusion serves as a reasonable assumption that

captures a type of well quantied `punishment' for bankrupts and allows researchers to calibrate a cost as-

sociated with bankruptcy. Such costs are a key to generating realistic solutions to models where households

trade-off such costs against the benet from a fresh-start (discharge of their debt). Similarly, another mo-

tivation for the exclusion assumption in these models is the fact that US Law prevents repeat bankruptcies

within an 8 year period and that bankruptcy of an individual is kept on their credit history records for 10

years.

More recently, however, there has been increased discussion about whether these assumptions are realis-

tic, followed by a move away from reliance on such assumptions. For example, Athreya and Janicki (2006)

evaluate the commonly used (but rarely justied) assumption that bankrupt individuals get excluded from

unsecured credit markets, as well as examine the quantitative role of exclusion in explaining the surge in

both consumer debt and personal bankruptcies. They conclude that such an assumption is hard to justify

from a theoretical perspective, especially without a better understanding of the income shocks households

facea key determinant of bankruptcies. This is because lenders have no incentive to punish borrowers

after bankruptcy unless bankruptcy reveals a change in their likelihood of repayment. Accordingly, only in

the case of small or primarily transitory shocks that exclusion penalties would have the most effect as the

option-value to borrow is much less when facing a permanent shock

Within the quantitative macro literature, Chatterjee et al. (2009) provide the closest study. They argue

that an exogenous credit market exclusion restriction is puzzling because a household that les for Chapter


7/37

that the average number of credit cards held by those with a past bankruptcy was 2.91 compared to 3.58 for

those without a bankruptcy. One of the most interesting ndings in Stavins (2000) is that the individuals with

prior bankruptcies have higher delinquency rates than the rest of the population, a nding that is suggestive

of the systemically different characterization of bankrupt individuals.

Musto (2004) and Fisher, Filer and Lyons (2000) provide other evidence in support of an exclusion

period. Musto (2004) analyzes the impact of the removal of the bankruptcy record from an individual's

credit record and shows that especially the credit-worthy individuals get more cards and see big jumps in

their credit limits. Indeed, such a nding is consistent with our results in that the high-credit individuals

here see a relatively larger `penalty' in the form of reduced credit lines and can thus have larger increases

at the time the bankruptcy ag is removed from the record. Using a panel study of households, Fisher,

Filer, and Lyons (2000) show that consumption of the bankrupt households depict higher sensitivity to their

incomes than in the period preceding the ling, which is consistent with binding borrowing constraints in

the post-bankruptcy period.

Unfortunately, these theoretical arguments or the indirect nature of the evidence so far presented in

empirical studies limit our ability to have a solid understanding of the basic facts surrounding households'

credit access after bankruptcy, a gap this paper hopes to ll. A very recent study by Han and Li (2009)

also analyze this question using data from the Survey of Consumer Finances and a different methodology

attempting to understand the equilibrium dynamics and disentangling changes in demand and supply.

Legal Literature

Outside of the economics literature, legal studies on post-bankruptcy rely primarily on available survey


8/37

that a very high percentage of individuals being offered unsecured lines of credit within a year of going

bankrupt. As well, she nds support for the `adverse event' theory of bankruptcy. She also notes that little

prior empirical work has been done, but that a number of authors have cited the need for more data and

evidence on the topic (see Braucher (2004) and Jacoby (2005)). In other work, Staten (1993) looks at the

role of post-bankruptcy credit on the number of bankruptcies. He draws his data from a survey as well, and

nds that one year post bankruptcy, 16.2 percent got new credit. Three years after, 38.6 percent obtained

credit. About half of each came in installment and revolving debt. However, highlighting the problems with

surveys and sample size, these numbers are quite different from the Porter (2008) results.

The background to the literature directly on post-bankruptcy lending is the work that has found that the

changes in the bankruptcy code enacted in 2005 made consumer bankruptcy more difcult to obtain, and

more expensive for the ler both in terms of ling costs and time allocation (Mann 2007, Sommer 2005).

Our question is about lending to consumers who have already led for bankruptcy. Porter (2008) de-

scribes the criteria that should apply, If even a modest proportion of bankruptcy debtors are untrustworthy

deadbeats who behave in immoral or strategic ways, the credit industry should be reluctant to lend to these

families. Indeed, individuals with low credit scores, dened as those individuals who have been unreliable

in repayment of debts, should not typically be a target of credit issuance. In a story that is consistent with our

ndings, Porter (2008), using a longitudinal study of bankrupt individuals, nds evidence that consumers

are `bombarded' with credit offers, including from the very issuers that have just had debts expunged. Over-

lain with this motive is evidence that more than a third of families post bankruptcy had worsening nancial

conditions, even accounting for the bankruptcy discharge (Porter and Thorne, 2006).


9/37

While these results are based on surveys alone, the patterns are largely consistent with our ndings.

3 A Simple Model of Creditor Decisions

3.1 Model Setup

To gain insight into why credit issuance may increase for some bankrupt borrowers, we draw on a stylized

model of debt valuation and lenders' decisions. The framework starts with a simple denition of debt from

a lender's perspective. The value of debt can be obtained as the weighted average, by the probability of

default, of two terms. The rst is the stream of risk free cash ows and second the recovery value in case of

default. In other words, the rst term is the value of debt when lenders know that individuals will repay their

debt for certain, so it can be valued as simply the discounted future value of payments using the risk-free

rate. The second term is the value of debt in case of default and can be obtained by multiplying the face

value of debt by the recovery rate and the exposure at default. Accordingly, the value of a debt to a lender

can be expressed as:

V = (1 P D)F V + P D (1 LGD) (EAD) B (1)

whereF V is the discounted future value of payments in the non-default scenario, P D is the probability

of default i.e. the likelihood of non-payment,LGD is the loss given default i.e. the percentage of losses

conditional on default,EADis the exposure at default i.e. the percentage of the face value of debt owed at

time of default, andB is the face value of debt. While the F Vcan have a complex form depending on the

type of debt, for our purposes we treat F V to reect the full credit line rather than the amount borrowed.


10/37

to-low credit scores, which mainly reect a borrower's debt holding and historical repayment behavior.

Accordingly, a lender considers the following four versions of equation 1:

V =

VPNB; VPB; V

SPNB ; V

SPB

where the superscripts P;SPrefer to prime and subprime borrowers, and the subscripts N B andB refer

to not-bankrupt and bankrupt, respectively. It is important to note that an individual can be in default of

payment but not bankrupt.

To distinguish between these four types of borrowers and to understand the protability of each type, we

now analyze each of the components of equation 1 in turn. Table 1 presents a summary of our assumptions

regarding each of these components. As we have already mentioned, we assumeEAD to be 100% for

all types. On the other hand,F V andB do vary across these four types of borrowers. However, we can

assume these terms to be equivalent across each type without loss of generality as part of a normalization

assumption. After all, the risk-free component of one dollar of riskless lending has equal future value for all

types of borrower. This claim is based on two assumptions which we think reasonable given the institutional

features of the credit card market. One, the length of contract loan is equivalent for each borrower. This

ensures that the discounted value of a $1 risk free loan is equivalent across types. Two, we assume that there

is a one-to-one mapping from probability of default to interest rate. This enables us to ensure that lenders

choosing a particular interest rate for a loan associates that loan with a particular default probability. Once

individuals are segregated into the four groups by observables, the loan rates are associated with type alone.

This leave us with two parameters that are key in for our story: the probability of default (P D) and loss


11/37

into bankruptcy is the change in the probability of default. In our simple model, we assume that ex-ante

subprime borrowers move marginally from high to higher default probability post-bankruptcy, while ex-

ante prime borrowers show a signicant increase in default probabilities on average. In other words, ex-ante

prime borrowers who le for bankruptcy look a lot more like a subprime borrower after they have led for

bankruptcy. This assumption is strongly backed by evidence from our data as shown in Figure 1, which

shows the 90-day delinquency rate for non-bankrupt and bankrupt borrowers in each of 5 credit categories

where the 90 day delinquency rate is used as a proxy for non-bankruptcy default. Note that the credit scores

listed on the x-axis correspond to the credit score of the bankrupt borrowers before their bankruptcy ling.

As for ex-ante subprime borrowers, the data show that these borrowers' delinquency / default rates are

largely unchanged after bankruptcy. These are, largely speaking, borrowers that were already at the bottom

of the credit quality spectrum and the shocks that lead to bankruptcy appear not to change their environment

to a great extent such that: P DSPB P DSPNB: For prime borrowers, however, the same data show a very

large increase in default: prime borrowers that go bankrupt have much larger default probabilities than prior

to ling, such that we can write: P DPB >> PDPNB . In fact, it is these large average changes and differences

in post-bankruptcy probability of default which help explain the relative decline in access to credit for prime-

borrowers post-bankruptcy that we observe in the data. This nding is also in-line with our prior belief that

bankruptcy is likely to carry a stronger signal about the post-bankruptcy repayment ability of ex-ante prime

borrowers: it is very likely that individuals who had higher ex-ante credit scores ended up in bankruptcy due

to a permanent shock, while those who are consistently around the low-end of the credit quality spectrum

might be more prone to frequent, transitory shocks.


12/37

simulation exercise to better capture the effects of changes in LGD across our borrower types on lender's

decisions.

Following these assumptions, we can now evaluate the relationship between debt values for each group

and make some claims about lenders' decisions to supply credit to these different groups.

Claim 1 From a lender's perspective, the value of an extra dollar lent to a subprime borrower who has gone

bankrupt is greater than one that is lent to a subprime borrower who has never gone bankrupt:VSPNB < VSPB :

To see this, we can re-write the debt value equation above for subprime borrowers who have never led

for bankruptcy:

VSPNB = (1 P DSPNB) + P D

SPNB

1 LGDSPNB

(2)

Recalling our assumptions thatLGDSPNB > LGDSPB andP D

SPB P D

SPNB;we can evaluate how equation

2 changes when this borrower becomes bankrupt. Breaking the equation into two parts, we can see that the

rst term decreases as individuals move to bankruptcy. However, this change is rather small because the

probability of default only slightly increases for these subprime borrowers as discussed above and as shown

in Figure 1:

(1 P DSPNB) (1 P DSPB )

However, the second term increases as both the probability of default modestly increases and the loss given

default decreases:

P DSPNB

1 LGDSPNB

< P DSPB

1 LGDSPB


13/37

VPNB = (1 P DPNB) + P D

PNB

1 LGDPNB

Given our assumptions and what we observe in the data, we can see that the rst term (1 P DPNB) de-

creases signicantly when a prime borrower enters bankruptcy as their post-bankruptcy probability of default

increases. On the other hand, the latter term, P DPNB

1 LGDPNB

, increases as probability of default in-

creases and loss given default declines. Again, we need to determine which one of the two terms has a larger

effect onVas prime borrowers move to bankruptcy. We can see in Figure 1 that the change in P DPNB to

P DPB is a very large oneon the order of 20%. So, we conjecture thatVP will fall as prime borrowers

enter bankruptcy unlessLGD changes on a very large magnitude.

3.2 A short simulation

We conduct two short simulation exercises to test the two conjectures seen above. As mentioned, the con-

clusions drawn rest on assumptions about the nature of loss given default for each type. In the prime case,

we posited thatVPNB > VPB unlessLGD changes by a large amount. In the subprime case, we claimed that

VSPNB < VSPB based on the assumption thatLGD

SPNB > LGD

SPB .

To illustrate these assumptions, we solve equation 1 for each of the four types based on known values

for probability of default (see Figure 1) and for all possible values ofLGD. We can then determine what

range of values ofLGD are needed to conrm the conjectures above. Figure 2 shows the results of two

simulations.

In the prime case, our exercise shows that there are no values ofLGD that permit in increase in VP as


14/37

framework presented helps us illustrate why the value of lending may be higher for subprime borrowers after

they have led for bankruptcy as opposed to lending to prime borrowers, especially since the latter become

more like a "subprime" borrower once they enter bankruptcy.

In the subsequent sections, we present data on credit availability pre and post bankruptcy for each type

of borrower. These empirical analyses support the post-bankruptcy conjectures discussed above. We nd

that while prime borrowers receive less credit after bankruptcy, subprime borrowers may indeed receive

more. Both of these are consistent with the value changes in the lender models above.

4 Data

Our analysis is based on a unique, very large proprietary data set provided by one of the three major credit

bureaus in the US. The data are drawn from geographically stratied random samples of individuals and

include information on variables commonly available in a personal credit report. In particular, the le

includes age, a variety of account and credit quality information such as the number of open accounts,

defaulted accounts, current and past delinquencies, size of missed payments, credit lines, credit balances, etc.

The information spans all credit lines, including mortgages, bank cards, installment loans and department

store accounts. The credit bureau also provides a summary measure of default riskan internal credit score.

As is customary, account les have been purged of names, social security numbers, and addresses to ensure

individual condentiality.

The primary data were drawn from two periods in time with an 18 month intervalJune 2003 and De-

cember 2004comprising a very large repeated panel with about 270 000 individuals For each individual


15/37

Unfortunately, we do not observe and therefore are not able to comment on the price or cost of available

credit to these individuals, which is likely to be an important indicator of credit availability. Nonetheless,

we believe our results are still informative and provide the rst direct evidence on credit access of bankrupt

individuals.

For the analysis we drop individuals that have a total credit limit smaller than $1,000 in year 2003. We

dene two sub samples. The rst one is the sample of individuals that have never led for bankruptcy,

comprising 122,159 individuals with complete information. Second, we construct the sub sample of indi-

viduals that go bankrupt between the two observation periods by selecting the individuals that have led

for bankruptcy in 2004 but had not declared bankruptcy before 2003 and, as a data cleaning exercise, drop

individuals that in 2004 report more than 18 months since last derogatory public record. Indeed, the number

of months permits to analyze the evolution of credit after bankruptcy across individuals.

Finally, we also use a larger and more recent panel dataset we have from the same credit bureau. This

panel, drawn in June 2006 and December 2007, helps us to analyze whether there might have been changes

in credit markets, especially as we entered the slow-down in this 2007/2008 crisis. In other words, we

use this latter dataset to see whether the associated credit cost for bankruptcythe ease at which bankrupt

individuals can get credithas changed between the credit boom period of 2003/2004 and the slow-down

in 2007. Unfortunately matching the two data sets is not possible and limits our analysis to a comparison of

two time periods as opposed to four.

Table 2 provides the summary statistics for the variables used in our analysis. Tables 3 and 4 provide

more detailed descriptive statistics on the average credit limit by credit score brackets for the whole sample


16/37

5 Empirical Methodology and Results

5.1 Estimation of the credit access cost of bankruptcy

We dene the credit access cost of bankruptcy (Credit Cost) as the difference in credit limit available to

individuals that have led for bankruptcy with respect to the credit limit that would have been available

to them had they not led for bankruptcy. This requires the estimation of a counterfactual credit limit for

individuals that le for bankruptcy. We exploit the time dimension of our dataset to estimate the bankruptcy

penalty of those individuals that le for bankruptcy sometime between June 2003 and December 2004 (ourtwo observation times). We proceed in three steps. First, using the sample of individuals that have never

led for bankruptcy in 2003 or 2004, we estimate the following model for the availability of credit in 2004

using observables in 2003 the results of which are provided in Table 5:

L_2004i= 1L_2003i+ 2X_2003i+ ui (3)

where i is dened for all individuals that have never led for bankruptcy and where X_2003 =fcreditscorei;

agei; numbercardsi; incomei; racei; etc:g is a vector of borrower characteristics in year 2003, and L_2004

and L_2003are the limits in 2003 and 2004 respectively. We emphasize here that this estimation is based on

our understanding of the process used by issuers to determine limits. Credit card issuers typically employ

credit bureau information to decide the amount of credit and terms offered, with the credit score itself often

acting as the most relevant variable in this decision. Therefore we can assume that, as econometricians, our

use of credit bureau information approximates the information set of credit card issuers.


17/37

Next, we estimate the credit cost of bankruptcy for individuals that led for bankruptcy between 2003

and 2004 by subtracting the estimated credit limit in (2) from the actual observed credit limit in 2004.

CreditCostj =L_2004j L_2004j

The credit cost of bankruptcy is negative when individuals obtain less credit after bankruptcy with re-

spect to the credit limit they would have had if they did not le.

5.2 Baseline Results

Figure 3 plots the average credit cost of bankruptcy against months since most recent derogatory public

record, which includes bankruptcy lings. As explained above, the credit cost is estimated as of December

2004 for the cross-section of individuals that le for bankruptcy between the two observation periods. By

examining the credit cost of bankruptcy of individuals in December 2004 with respect to the number of

months since they led for bankruptcy we can make inferences about how credit availability changes overtime after bankruptcy. We observe a U-shaped pattern, with a decrease in available revolving credit during

the rst six months after ling for bankruptcy, as would be expected. The credit limit loss reaches its

maximum ve months after bankruptcy and is on average $24,000 at that point. After that, the credit cost

gets smaller and approaches $15,000, on average, at 18 months after bankruptcy.8 Unfortunately, we cannot

calculate the credit cost beyond 18 months after bankruptcy due to data limitations. Similarly, notice also

that the observed decline in the rst months may just reect the reporting lag to the credit bureau. Due to

data limitations we cannot produce this gure using the 2006-2007 data (variable months since bankruptcy


18/37

(a positive credit cost) by credit score. For a signicant fraction of individuals (18.3%) the credit cost of

bankruptcy is indeed positive, meaning that they actually get more credit than predicted by model (3). This

gure illustrates the phenomenon that we highlight; those with very low credit quality are much more likely

to receive increases in credit.

In Table 6 one can observe that individuals with the lowest credit scores have, on average, a positive

bankruptcy credit cost. We measure this `benet' to bankruptcy at $300 of increased revolving credit. While

this increase is only 5.9% of the average credit limit prior to bankruptcy for the group of individuals, it is

notable for the fact that it is positive. Importantly, this $300 reect an average consumer experience, rather

than a few outliers. Indeed, 65% of individuals in the lowest credit score group have a positive bankruptcy

credit cost.

We interpret these results as supporting a credit supply story of bankruptcy along the lines of Dick and

Lehnert (2009). Increased lending to low credit quality borrowers post bankruptcy provides a potential

reduction in the deterrent to le for these individuals. In spite of the widely believed exclusion from creditmarkets, a default by a low credit quality borrower had a relatively small impact.

5.4 Heterogeneity: Credit Cycle

We next explore the degree to which our results are a function of the credit cycle. The 2003-04 period is

one that has been characterized as a credit boom; indeed one that likely had particularly lax credit standards.

Potentially then, credit was easy to obtain both before and after bankruptcy. This section will evaluate how

well our results hold up in a more restrictive credit environment.


19/37

has an impact on the consequences of ling, and therefore, determines the propensity to le. Consistent with

their results we also nd that different credit quality individuals are impacted differently by the credit cycle.

We can use the bank lending framework we presented in Section 3 to interpret these empirical results.

We can see that our ndings are consistent with (1) a small change in the PDs and LGDs of prime borrowers,

which makes them as protable as before, and (2) a signicant increase in the PDs and/or increase in LGDs

of subprime borrowers after bankruptcy in a downturn, which makes them a less protable option than

similar subprime non-bankrupt borrowers. Unfortunately, the time period of our sample only captures the

beginning of the current downturn period in December 2007. Further research is needed as more recent data

becomes available.

5.5 Heterogeneity: Other Factors

Combining data from the US Census on characteristics of the neighborhoods of these individuals, Table 7

shows that individuals with positive credit cost tend to live in areas with lower educational attainment, higher

divorce rates, more blacks, and lower incomes. To further investigate these trends, we present in Figure 6

the percentage of individuals with positive penalty by credit score, dividing the sample by percentage of

minorities (Panel A), income brackets (Panel B) and education level (Panel C) of the neighborhoods of these

individuals. We present the results for both 2003-04 and 2006-07. We can observe that individuals with the

lowest credit score and a lower propensity to repay as proxied by income, race and education are the ones

that are offered more credit after bankruptcy, especially in the 2003-04 period. These ndings are consistent

with the observation that lenders target riskier borrowers. In credit card industry parlance these individuals


20/37

by type of credit changes as a function of credit quality. For example, if low-risk individuals are more likely

to apply for credit cards and high-risk individuals for auto-loans.

Accordingly, we repeat our analysis on the bankruptcy credit cost for other types of creditmortgages,

installments loans (including auto-loans), and total credit. Figure 7 presents the results from this exercise.

The gure shows no evidence of the composition effects mentioned above and that total credit and mortgages

follow a similar pattern to those observed using revolving credit alone. Having said so, interpreting the

changes in secured lines, such as mortgages, is difcult especially because only unsecured debt is discharged

in bankruptcy and not secured loans. Nonetheless, it is interesting that installment credit shows a different

picture: a smaller fraction of low credit score individuals have a positive credit cost, as compared to other

credit types, while the percentage of individuals with a positive installment credit cost is quite stable across

the credit score dimension. This is again consistent with the patterns reported in Porter (2008) for secured

lending and is likely driven by other supply factors, such as differences in underwriting standards between

secured vs. unsecured loans.However, the fact that low credit-score individuals get more unsecured lending than secured remains a

puzzle. One would expect that secured lending, which is generally considered to be a lower risk channel,

would be more easily obtained in a high-risk context. We encourage future research on this topic.

6 Potential caveats

As is standard, there are a few factors that confound our interpretation of these observed facts. Among

these is the identication of supply vs demand effects Recall that one of our central ndings is that


21/37

Similarly, it may well be that, well-educated individuals and/or those with ex-ante good credit histories

are better at reading the ne print on solicitations they receive compared to others, and less likely to accept

credit limits at any cost. Accordingly, lenders might well be targeting all bankrupt individuals but only those

with low-credit scores accept the offers, explaining the observed patterns in our data.

However, both of these explanation are difcult to justify in an equilibrium framework. In such an

environment, one would expect lenders to respond to react; however, the legal literature provides ample

evidence that all types receive continued solicitations for credit after bankruptcy. This suggests that our

results emerge from differences in the provided limits rather than systematic demand differences amongst

the borrowers.

Despite the fact that we cannot disentangle these demand factors from supply and even if the differential

access is due to differences in demand, our initial nding about the provision of credit across the board

still suggests that lenders seem to target bankrupt individuals. In other words, whether lenders are targeting

riskier, sub-groups of individuals or not, they certainly do not seem to be shy about lending to individualsshortly after bankruptcy. This is consistent with the survey evidence provided by Porter (2006) on targeted

solicitations of recently bankrupt individuals by lenders, as discussed in Section 2.

7 Conclusion

This paper presents, to our knowledge, the rst direct evidence on credit access of individuals post-bankruptcy,a topic that has generated much discussion and speculation in economics and other literatures. We rst show

that while individuals do see signicant drops in their credit lines immediately after they le for bankruptcy


22/37

Nevertheless we need more analysis to resolve some of the confounding issues to have a clearer, stronger

picture. In particular, we need a better understanding of the nature of income shocks or other factors that

derive an individual's bankruptcy decision. After all, such an understanding is the key to whether bankruptcy

reveals a change in an individual's future repayment behavior. Similarly, using longer time-series data it

will be interesting to see how the exclusion credit cost might have changed over the last couple decades

and whether credit availability for recently bankrupt individuals will change as part of the ever changing

landscape associated with the current nancial turmoil, as hinted by some of our results based on limited

data from 2007.

References

[1] Aguiar, Mark, & Gopinath, Gita, 2006, Defaultable Debt, Interest Rates, and the Current Account,

Journal of International Economics, 69(1), 6483.

[2] Athreya, Kartik B., 2002, Welfare Implications of the Bankruptcy Reform Act of 1999, Journal of

Monetary Economics, 49(8), 156795.

[3] Athreya, Kartik B., 2004, Shame As It Ever Was: Stigma and Personal Bankruptcy, Federal Reserve

Bank of Richmond Economic Quarterly, 90(Spring), 119.

[4] Athreya, Kartik B., 2006, Credit Exclusion in Quantitative Models of Bankruptcy: Does it Matter?

Federal Reserve Bank of Richmond Economic Quarterly, 92(Winter), 1749.


23/37

[10] Fisher, J., Filer, L., & Lyons, A. C., 2004, Is the Bankruptcy Flag Binding? Access to Credit Markets

for Post-Bankruptcy Households, American Law & Economics Association 14th Annual Meeting,

April, Working Paper 28.

[11] Jacoby, M., 2005, Ripple or Revolution? The Indeterminacy of Statutory Bankruptcy Reform, Amer-

ican Bankruptcy Law Journal, 75, 169190.

[12] Han, Song, & Geng Li, 2009, "Household Borrowing after Personal Bankruptcy," Finance and Eco-

nomics Discussion Series 2009-17, Board of Governors of the Federal Reserve System.

[13] Livshits, Igor, MacGee, James, & Tertilt, Michele, 2007, Consumer Bankruptcy: A Fresh Start,

American Economic Review, 97(1), 402418.

[14] Mann, Ronald, 2007 Bankruptcy Reform and the Sweatbox of Credit Card Debt, Illinois Law

Review, 375.

[15] Musto, David K., 2004, What Happens When Information Leaves a Market? Evidence from Post-

bankruptcy Consumers, Journal of Business, 77(4), 725748.

[16] Porter, Katherine, 2008, Bankrupt Prots: The Credit Industry's Business Model for Postbankruptcy

Lending, Iowa Law Review, Vol. 94.

[17] Porter, Katherine and Deborah Thorne, 2006, The Failure of Bankruptcy's Fresh Start Cornell Law

Review, Vol. 92.


24/37

[22] Sullivan, Theresa, Warren, Elizabeth & Westbrook, Jay 2006, Less Stigma or More Financial Distress:

An Empirical Analysis of the Extraordinary Increase in Bankruptcy Filings, Stanford Law Review 59

(213).

[23] Weiner, Richard, Baron-Donova, Corinne, Block-Lieb, Susan & Gross, Karen, 2005 Unwrapping As-

sumptions: Applying Social Analytic Jurisprudence to Consumer Bankruptcy Education Requirements

and Policy. American Bankruptcy Law Journal 79 (2).


25/37


26/37

FIGURE 2: VALUE OF BANKRUPT VS. NON-BANKRUPT SUBPRIME AND PRIMEBORROWERS

Bankrupt LGD

Non-bankrup

tLGD

Panel A: Value of Bankrupt vs Non-bankrupt Prime Borrowers

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

tLGD

Panel B: Value of Bankrupt vs Non-bankrupt Subprime Borrowers

0.6

0.7

0.8

0.9

1


27/37

FIGURE 3: AVERAGE CREDIT COST BY MONTHS SINCE FILING

(in thousands of dollars)

Note: Solid line indicates 3-month moving average, dots indicate the average bankruptcy credit cost if filed bybankruptcy X months ago. Methodology for calculating the credit cost is discussed in Section 4. X-axis indicates time

since bankruptcy. Y-axis indicates change in credit available versus counterfactual of similar individuals who did notdeclare bankruptcy in thousands of dollars.


28/37

FIGURE 4: CREDIT COST BY CREDIT SCORE

(in thousands of dollars)

Note: Methodology for calculating credit cost is discussed in Section 4 of the paper. X-axis indicates credit score inyear preceding bankruptcy. Y-axis indicates change in credit available versus counterfactual of similar individuals who

did not declare bankruptcy in thousands of dollars.


29/37

FIGURE 5: FRACTION OF INDIVIDUALS WITH INCREASEIN COUNTERFACTUAL

CREDIT FOLLOWING BANKRUPCY (POSITIVE CREDIT COST)

Note: The figure shows the number of individuals who had more credit than would have otherwise been availabledivided by the total number declaring bankruptcy, for a particular credit score. The line indicates the moving average

across 100 of these credit score groups. Methodology for calculating the credit cost is discussed in Section 4. X-axisindicates continuous credit scores.


30/37


CREDIT FOLLOWING BANKRUPCY (POSITIVE CREDIT COST)PANEL A: RACE

PANEL B: INCOME PANEL C: EDUCATION


31/37


CREDIT FOLLOWING BANKRUPCY (POSITIVE CREDIT COST)PANEL A: TOTAL CREDIT LIMIT

PANEL B: INSTALLMENT CREDIT LIMIT PANEL C: MORTGAGE LIMIT


32/37


33/37

PANEL A: COMPLETE SAMPLE (N = 122,159)

CREDIT LIMIT


34/37

PANEL A: COMPLETE SAMPLE (N = 949,976)

CREDIT LIMIT


35/37

Dependent Variable Total Credit

Limit

(2004)

Revolving

Credit Limit

(2004)

Total Credit

Limit

(2006)

Revolving

Credit Limit

(2006)

Total Credit Limit 0.904*** 0.0503*** 0.984*** 0.0367***

(0.00621) (0.00209) (0.00162) (0.000552)

Installment Credit Limit 0.102*** -0.0268*** 0.0950*** -0.0181***

(0.0187) (0.00629) (0.00728) (0.00248)

Revolving Credit Limit 0.167*** 0.895*** 0.0204*** 0.874***

(0.0143) (0.00482) (0.00401) (0.00136)

Revolving Credit Utilization -0.120*** -0.0322*** -0.0795*** -0.0226***

(0.0153) (0.00515) (0.00528) (0.00180)

Revolving Credit Balance 0.871*** 0.0595*** 0.577*** 0.0115*

(0.0636) (0.0214) (0.0187) (0.00635)Revolving Credit Balance - Squared -0.00887*** -0.00274*** -0.00407*** -0.00160***

(0.000730) (0.000246) (0.000152) (5.17e-05)

Credit Score 0.0385*** 0.0207*** 0.0201*** 0.0138***

(0.00403) (0.00136) (0.00142) (0.000483)

Bank Cards (number) 0.787*** -0.526** 1.244*** 1.759***

(0.0702) (0.208) (0.0768) (0.0261)

Ratio of Revolving to Total Credit Limit 16.84*** 1.700*** 10.10*** -0.333*

(1.472) (0.496) (0.510) (0.173)

Ratio of Installment to Total Credit Limit 18.60*** 3.791*** 20.93*** 2.862***

(1.835) (0.618) (0.661) (0.225)

90-Days Delinquent (Current) -11.62*** -0.877 -14.02*** -2.303***

(2.284) (0.769) (0.842) (0.286)

90-Days Delinquent (Ever) -0.464 -0.300 -0.810** -1.244***

(1.024) (0.345) (0.400) (0.136)

Age-Squared -0.0108*** -0.00345*** -0.0110*** -0.00320***

(0.000809) (0.000272) (0.000693) (0.000236)

Age 0.353*** 0.265*** 0.342*** 0.259***

(0.0873) (0.0294) (0.0582) (0.0198)

Divorced (Female) 19.69** -1.549 -7.606** -13.21***

(9.090) (3.062) (3.445) (1.172)

Divorced (Male) -5.259 -9.824*** - -

(10.51) (3.540)

Greater Than High School Equivalency (Female) 47.98*** 5.886*** 34.71*** 7.872***(3.973) (1.338) (1.633) (0.556)

Income Growth 0.648*** 0.148*** 0.406*** -0.00734

(0.103) (0.0348) (0.0384) (0.0131)

2003/2004 2006/2007

TABLE V: REGRESSION RESULTS (NON-BANKRUPT INDIVIDUALS)


36/37

Credit Cost

($ thousands)

Positive Credit

Cost(% of cohort)

Credit Cost

($ thousands)

Positive Credit

Cost(% of cohort)


37/37

Negative

Credit Cost

Positive Credit

Cost

Difference Negative

Credit Cost

Positive Credit

Cost

Difference

PANEL A: 2003/2004Black (% in 1 mile radius) 0.1193 0.2196 (0.1003122)*** 0.0333 0.0601 (0.0267724)

Greater Than High School Equivalency (% in 1 mile

radius) 0.8088 0.7651 0.0436923*** 0.8277 0.7830 0.0447033***

Divorced (% females in 1 mile radius) 0.1153 0.1235 (0.0081883)*** 0.1154 0.1242 (0.0087478)***

Divorced (% males in 1 mile radius) 0.0936 0.0993 (0.0056993)*** 0.0912 0.0986 (0.0073737)***

Public Assistance (% in 1 mile radius) 0.0338 0.0463 (0.0124632)*** 0.0249 0.0329 (0.0080182)***

No Earnings (% in 1 mile radius) 0.1878 0.2069 (0.0191085)*** 0.1813 0.2051 (0.023841)***

Income Growth (in 1 mile radius) 0.3351 0.0460 0.2890982*** 0.0733 -0.1634 0.2367139***

Hispanic (% in 1 mile radius) 0.1075 0.1101 (0.0026069) 0.0414 0.0312 0.0102375***

Median Household Income 43,269 40,700 2569.149*** 42,143 40,736 1407.5***

Bank Cards (number) 3 1 2.3791083*** 3 1 2***n = 1170 n = 262 n = 1170 n = 262

PANEL B: 2006/2007Black (% in 1 mile radius) 0.1216 0.1874 (0.0658803)*** 0.0373 0.0623 (0.025056)

Greater Than High School Equivalency (% in 1 mile

radius) 0.8119 0.7824 0.0295308*** 0.8347 0.8010 0.0337***

Divorced (% in 1 mile radius) 0.1042 0.1114 (0.0071866)*** 0.1030 0.1107 (0.0077)***

Public Assistance (% in 1 mile radius) 0.0329 0.0402 (0.0072722)*** 0.0233 0.0292 (0.0059115)***

No Earnings (% in 1 mile radius) 0.1872 0.2069 (0.0197069)*** 0.1822 0.2044 (0.022205)***

Income Growth (in 1 mile radius) 0.7270 0.5021 0.2249104*** 0.2976 0.1112 0.1864375***

Hispanic (% in 1 mile radius) 0.1047 0.0961 0.0086499*** 0.0362 0.0309 0.0052895***

Median Household Income 48,828 45,588 3240.82*** 46,456 43,993 2463***

Bank Cards (number) 3 1 2.3150063*** 3 1 2***n = 4594 n = 815 n = 4594 n = 815

MEDIAN

Notes: Based on authors' calculations using credit bureau data, Census, and the American Community Survey. The values reported pertain to individuals who declared bankruptcy

between 2003 and 2004 (Panel A) and between 2006 and 2007 (Panel B). Each sample is partitioned into two groups: positive credit cost and negative credit cost. The statistics

reported are the mean and median values for each of the demographic measures in the row heading. We adopt the usual convention: *** p

Documents

Forgive and Forget - Who Gets Credit After Bankruptcy and Why